Here’s Why Every Product Manager Must Learn To Love Data

“Big data.” It’s one of Silicon Valley’s favorite buzzwords to hate, yet there’s no data shortage in sight as data companies continue sprouting (and growing) like weeds. It’s clear that data is here to stay, but many product managers are still yet to embrace data-driven product management…and they’re making a big mistake.

Here’s why every product manager should start paying more attention to data…yesterday:

It’s Not as Complicated as it Looks

Not being a “math person” or a data scientist is no longer a valid excuse to diss data. There’s not much actual math involved in data-driven product management as the vast majority of tools out there right now come equipped with analytics packages that collect and present data in an easy to understand format. Thanks to these powerful analytics tools, you do not need to be a data scientist to collect and analyze data anymore, and according to a recent piece published by Harvard Business Review, you don’t even have to be a “math person” to make smart data driven decisions. The HBR article, which I highly recommend every self-identified “non-math person” read, suggests anyone without a basic understanding of statistics take a refresher, while noting that said refresher does not mean going back to school and relaying Nate Silver’s advice to take a hands on approach to learning about data.

“Getting your hands dirty with the data set is, I think, far and away better than spending too much time doing reading and so forth.”

And even if you prefer to take the textbook approach to data, an abundance of resources have emerged alongside the recent surge in data companies, so people with little-to-no experience to learn how to collect, interpret, and apply data. Take for example, Mind the Product’s guide to analytics for product managers, or if you prefer to see the big picture first, something like a Big Data Fundamentals course might help get you started.

Product Management Careers are Trending Toward Data

Remember when marketing was almost entirely driven by creative initiatives? Marketing is now a data-heavy trade, where every creative proposal needs to be supported by data. Meanwhile, product management, along with many other trades are undergoing a very similar shift where data literacy is no longer just a “nice-to-have” skill, but one of the most important skills of the century. Just take a look at the job descriptions that come up when you search “Product Manager,” in your preferred job search engine, it’s difficult to ignore the cries for “analytics” and “insight,” in even the most junior roles.

But what about good ole’ PM intuition, you ask? While intuitive decision-making will always exist, product managers who rely solely on their intuition are soon to be a thing of the past in the wake of big data and data-driven decision making, as Tomas Chamorro-Premuzic recently wrote:

“Purely intuitive managers may face extinction only if they ignore the valuable information provided by data. At the same time, those managers who are capable of data-driven intuition will remain in demand, and increasingly so.”

So even if your PM spidey sense is excellent it’s simply not enough to keep up with larger trends, but in tandem with data literacy it could make you a better product manager and an even greater asset to your organization.

You Can Use Data to Make Better Roadmap Decisions

While unsolicited customer feedback helps product managers understand only the vocal minority (the customers who voluntarily share their thoughts and ideas), data provides an objective look at every customer, making it an an excellent way to learn more about who your customers actually are and how they’re using your product. Knowledge is power, right?

Product managers can make more well-rounded product roadmaps by relying on a combination of feedback and data; customer feedback is fuel for ideas, while data is fuel for decisions. For example, when deciding between updating an old feature or adding a new one based on several customer suggestions you can look at the lifetime value (LTV) of the customers who’ve requested the update and the LTV of customers who’ve asked for a new feature and determine which initiative would be most valuable to your company. (This is of course, an oversimplification as you’ll also want to pull other data as well). Later, whether you decide to update the feature or to implement a new one, you’ll be able to monitor its usage and impact on customers and determine whether it’s been successful.